The latest national retail traffic report from Experian FootFall shows a 5.3% week on week increase in shopper traffic and a decline of 3.4% year on year, which is slightly better than the year to date shortfall of 4.3%.

Each day last week produced a single figure decline on last year, possibly as a result of the reasonably buoyant conditions which followed the Diamond Jubilee celebrations of 2012.

Compared with last year, the Eastern England region alone saw climbing retail traffic volumes, up 2.1%. Ten out of the 11 regions saw footfall levels fall below those for the same week last year. In contrast, their weekly figures were higher than last.  The only exception is the North East region which over several reports has enjoyed a positive week on week performance, which this week fell by1.8%.

Shoppers rallied again to produce growth in the Retail Park Index, both week on week and year on year by 4.9% and 2.9% respectively

Read the full report

Altrincham has become the top location for identity fraud in the UK outside certain parts of London.

The leafy Cheshire town recorded 13 fraud attempts for every 10,000 adults, with residents targeted at over three times the national average (four in every 10,000 adults). This compares with 11 attempts per 10,000 adults for London as a whole, although there are areas of the Capital with significantly higher rates of identity fraud.

Altrincham’s Greater Manchester neighbour Stretford, also appears in the UK’s top ten target location for fraudsters committing identity theft.

London as a whole experienced 11 attempts for every 10,000 adults, but with significantly higher rates in some inner-city boroughs such as East Ham which recorded 27 fraud attempts for every 10,000 adults in 2012 – the UK’s highest – with individuals targeted at almost seven times the national average. Residents in Romford and Bexleyheath were also targeted at around five times the national average. Additional hotspots are to be found in the London commuter towns of Hatfield, Dartford and Camberley although the previous rise in fraud in Thames Valley and the Home Counties has slowed overall in 2012.

Our Mosaic geo-demographic profiling analysis reveals that victims of identity theft are highest among the inner-city ‘Terraced Melting Pot’ group, typically those living in mixed urban neighbourhoods with low to middle incomes.

The research also reveals that identity fraud victims are being targeted from more affluent towns in the vicinity of the UK’s major cities such as Manchester, Birmingham and London.  These residents fall into the ‘New Homemakers’ Mosaic group, and comprise younger couples living in new starter homes on the outskirts of major towns and cities, typically with average incomes and a high demand for credit.

It comes as no surprise that the Capital remains a target for third-party fraudsters – particularly around densely populated inner-city boroughs where identities are easier to steal. However, fraudsters are clearly attracted to rich pickings in more affluent areas, where access to an identity might be harder to obtain but the prize makes it worth the extra effort. In this respect, more affluent locations in close proximity to major cities such as Altrincham in Cheshire, Sutton Coldfield in the West Midlands, Hatfield and St Albans by London, have become key targets for identity thieves. Clearly identity theft is not just confined to inner-city areas but is a UK-wide problem and a symptom of tougher economic times, highlighting the need for people everywhere to be increasingly vigilant.

Our analysis of first-party fraud shows that, generally, it is those on low incomes, with thin or empty credit files, who are attempting to ease their financial position by misrepresenting applications or making exaggerated claims over their income and personal circumstances. However, a significant proportion of first-party fraud is now being perpetrated by the ‘Liberal Opinions’ Mosaic group. Typically these are young, well educated professionals living and working in London and other major cities.

Across the financial services sector as a whole, it is vital that firms and other providers of credit recognise the financial and reputational risks associated with fraud, and put in place increasingly sophisticated identity verification and anti-fraud measures to combat the threat. Fraud prevention and detection tools which allow organisations to detect, monitor and assess risk will help firms identify anomalies within applications and check for signs of adverse credit histories. Individuals also have a role to play fighting the fraudsters by taking steps to protect their personal information.

The Fraud Index is based on data derived from National Hunter and Insurance Hunter, the UK’s leading fraud prevention systems, operated by Experian on behalf of members. These systems enable financial institutions to cross-match applications against over 100 million previous application records in order to spot commonalities and anomalies that are potentially indicative of fraud for further investigation.

Data from the census shows us the change in industrial structure across the nation and reminds us of the importance of keeping our accounting frameworks up-to-date.

July 31st will see the release of the 2013 Blue Book and incorporate the annual changes to the methodology used to calculate GDP – the benchmark on which the world’s economies are judged and to which the national press gives much coverage.

This means that most data will be revised in some way. From the start of the series to 2007, data will incorporate methodological improvements as well as partial supply-and-use rebalancing. From 2008-2011 the data will be fully open to revisions from both those sources stated for the earlier period as well as new data sources and a full supply and use rebalance. From 2012q1 – 2013q1 the data is fully open to revisions as well as the usual quarterly round post Supply and Use balancing. The base and reference year will also move on one year to 2010 so all series which are given in real terms will necessarily change given new deflators.

The key changes in methodology include:

• Improvements to Gross National Income.
• Alignment of National Accounts and Public Sector Finances whose methodology is subject to change every quarter.

Next year, with the release of the BB2014 the UK data will be moving to be consistent with the new European System of Accounts (ESA10). This will make improvements in dealing with the financial sector, globalisation, asset changes, pensions, sector detail and a lot more.

Although numbers changing every year is not ideal, the ONS is not only obliged to do so by an EU requirement, but it makes sense to try to capture the changes to the economy over time.

In our view, we support the slight tweaks on a yearly basis rather than a big jump every five or ten years. To demonstrate this, figure 1 shows the industrial structure of the economy over time, according to Census data on employment.
Aside from the steady decline in agriculture share, the big change occurred in the 1970’s when manufacturing began a sharp decline, mirrored by a steep increase in services. This change was not a one-off break in the structure of the economy but an on-going shift. Even since 1991, services’ share of employment has increased from 68% to 81% in 2011 and so it is vital that the accounting classification keep up with the shifting landscape of our economy.

The move from Standard Industry Codes (SIC) 2003 to SIC2007 made a significant update to sector definitions. It introduced much greater coverage in services, including amongst other things, new dedicated allocations to Computing & Information Services.

In turn, many manufacturing categories were disbanded.

As a result, many series are not available before 1997 at the moment but the ONS are working on this and as soon as it is available, we will include this data in our services. In terms of the blue book 2013 and shift to ESA10, we will stay in line with the officially published data as and when it becomes available.

As a side-note to this, the chart illustrates the heavy reliance that the UK now has on services (public and private) and why perhaps the “march of the manufacturers” may be of benefit if in high-skilled industries but has much smaller role in the immediate recovery from the current economic slump than services.

To see more economics weekly topics of focus click HERE or follow the tag #Economics WTOF.

Telcos today are facing lot of challenges to manage the portfolio risk. This is largely due to the missing link of the Credit and Risk management in the entire customer life cycle. In telecommunications this has historically generally not been the case; however, with the continued expansion of products and services available to mobile users, such as Exposure limits, M-payments and E-wallets, it has become more important that the Credit Risk and Collections team has accountability to the portfolio risk and a high profile within the company’s Senior Management teams.

The environment of Telcos has changed with growing functionalities to do banking and making payment transactions from mobile phones. This is forcing the Telco companies to think differently to plan strategic initiatives to overcome the major challenges:

• Organization structure lacks a dedicated department accountable for credit and risk related decisions.
• Absence of Integrated Data warehouse/ Data mart to have easy access of the data across the customer lifecycle.
• Standard KPIs not in place for performance measurement and benchmarking
• Absence of structured Reporting Tool and Management Information System
• Statistical scoring Models and Analytical Framework not in place
• Risk segmentation strategies not defined appropriately
• Legacy systems for Vetting, Customer Management, Collections and Fraud
• Integration with Fraud Control.

Organization structure lacks a dedicated department accountable for credit and risk related decisions.

The organizational structure of most telecom companies does not have an independent dedicated Credit and Risk management unit to design policies and processes on Vetting, ongoing Customer Management, Collections and monitoring them on an ongoing basis. The best practice approach is to have distinctive advantages of end to end lifecycle picture, balanced view on accounts and customers, profitability based model for acquisitions and customer management, understanding of legal and compliance issues, long term strategies based on economical, market and customer trends.

Overall risk ownership is missing throughout the customer life cycle of Vetting, Customer Management, Collections and Fraud. It is observed that the mentioned functions and processes are independently managed by the respective departments and not accountable for any gaps i.e. in case there is a gap in the vetting process, a wrong account has been activated, a deactivated account reactivated etc. the respective departments will be held responsible, but there is no dedicated Credit and Risk department to set risk triggers on critical processes and work as third eye to monitor the entire customer life cycle.

Absence of Integrated Data warehouse/ Data mart to have easy access of the data across the customer lifecycle.

A dedicated credit Data Warehouse (DWH) / Data mart) that should include all the customer information which is relevant for risk and exposure measurement, monitoring and management at subscription/ customer and portfolio level. This DWH should act as the primary source to feed all Vetting, Credit management and Collections processes

Standard KPIs not in place for performance measurement and benchmarking

• The organization should have standard KPIs defined separately for every phase across the customer life cycle E.g. for each portfolio and segment vetting decisions, Offers and Terms of Business, Credit Control (re)-organization, detailed operational and strategic KPIs optimizing Customer Life Time Values, revenues adjusted on Risk measures etc
• This organization should be fed with enhanced analytics and simulation capabilities simulating expected outcomes of changes in credit strategies at all levels: portfolio level but also every customer sub-segment and offer level
• The set of reporting KPIs should be able to cover both the business risk control at portfolio level and the operational performance e.g. performance of risk factors driving the decision, effectiveness of policy rules, effectiveness of scorecards and score ranges ageing reports, number of never paid customer by registration month etc.

Absence of structured Reporting Tool and Management Information System

The absence of a structured Reporting Tool and MIS results in the manual generation of reports. This is highly time consuming blocking valuable resources thereby delaying the decision making. Implementation of a comprehensive reporting tool to support both pre-defined and bespoke reports in an automated environment will minimize the report generation time and invest the same for analysing and taking strategic decisions.

Statistical scoring Models and Analytical Framework not in place

The current risk assessment criteria is based on expert rating models and a number of policy rules determining the decision on Accept, Decline, Referral and Deposit strategies. However, there is no statistical model in place for scorecards and decisioning. The most effective best practice is that the overall system (models, rules and strategies) should be assessed, optimized and monitored regularly following a clear analytical and statistical framework for each phase of customer life cycle.

The strategy design should also measure and balance on agreed strategic KPIs: Risk, balance usage, churn in order to ensure and help the organization achieve its objectives. Greater confidence in the system will lead to greater automatic decisioning (and then less manual intervention) and more effective strategies.

Risk segmentation strategies not defined appropriately

The credit segmentation model of most Telcos seems too unsophisticated to deal with the current complexity of customer base and with the growing pressure of bad debt. A statistical predictive risk behavioural scoring would allow greater differentiation in most of the credit management processes (e.g. Limit Setting, High Spend) improving their effectiveness (i.e. reduced bad debt through reduced exposure to riskier customers and reduced operational cost through reduced manual activities) and improved customer experience (i.e. reduced voluntary churn). This segmentation shall drive CRM cross-/ up sell campaigns, product and tariff planning, retention strategies etc. Based on local legislation if possible, the credit bureau scores can be incorporated for a robust model based on internal & external info.

The collections scoring models are mostly expert rating systems and not statistical in nature. To effectively manage the collections portfolio and optimize the resources, it is important to have statistical scoring models beginning 1Dpd onwards predicting the risk on the account/ customer level. Those accounts with no prior history will be worked based on the risk scores at application level and behaviour level. The segmentation model by customer groups should include risk assessment of “High”, “Medium”, “Low“ and “Premium”

Adopting a finer segmentation of collection paths and actions based on risk/exposure, an organization can achieve:

• larger collections/ recovery rates in less time (through tailoring actions to each customer’s specific profile)
• lower operational cost reducing manual and ineffective activities
• reduced churn rates from low and mid risk customers disgruntled by inappropriate collection treatments
• reduced disconnections and barring of outgoing calls adopting softer strategies for low risk customers and allowing them to self cure.

Account level Management to Customer Management

Transition from the existing account/ subscription management model to the best practice model of customer management that incorporates risk assessment at account and customer level thereby driving credit limits management to optimally manage the portfolio for maximizing revenue and minimizing losses. The customer management system will introduce adequate usage management (to prevent Bill Shock).

The best practice model is to calculate and assign initial customer level limits at the time of application (based on application scores on risk assessment) and manage the same as ongoing customer credit management process throughout the contract period. The risk behavior scores will drive the ongoing limit management process to increase/decrease the limits corresponding to the risk levels. Business objective will remain to limit losses without affecting real usage spends.

Legacy systems for Vetting, Customer Management, Collections and Fraud

The systems for Vetting, Customer Management and Collections are legacy systems and mostly disintegrated from one another thereby breaking the flow of the information during the customer life cycle. The legacy systems also lack flexibility and the scalability for changes in future to manage the increasing volumes.

The system should be modern enough to include Champion Challenger functionalities i.e. Implementation of proper tests, control and documentation process (i.e. random or specific sample selection – monitoring of outcome – implementation of successful strategies) within the current test environment. The system must have simulation capabilities to simulate expected outcomes of changes in Vetting, Customer management, Collection policies and strategies at customer and portfolio level for future existing, better or worse case scenarios.

The functional objective of the Customer Management Solution for Telco’s is to deliver account level solutions that can be used to effectively manage account relationships aggregated at customer level and automate many current decision making processes. The customer management solution should offer:

• Sophisticated customer and portfolio management technology.
• Powerful linking capabilities to provide holistic customer view – over time.
• Powerful analysis and optimization.
• Regulatory compliance management.
• Base reporting and insight.
• Up-selling the right service at the right time to the right customers.

A standard dedicated and automated collections system using end-to-end work flows and full customer view to manage all delinquent cases in-house and also for the DCAs using multiple account treatments, driving or supporting operator activity. This is to ensure standardization and proper controls in the hands of Business. E.g. Historical actions of DCAs will be visible to the Business; status of No contacts/ wrong phone after numerous attempts can be tried for different treatment; Return post info. will be visible to get the correct address.

Integration with Fraud control

The focus of the Fraud department in Telcos is mostly on Post-activation fraud. The best practices emphasize to tighten the processes to control any fraudulent activity (identity fraud etc.) at Pre-activation stage of application processing. Early Warning Indicators in collections to identify post activation potential fraudulent customers and escalating the same to the Fraud department. E.g. Accounts/ Customers with just less that 3 mob(months on books) and missing the 1st/ 2nd payments. Never payers or 1-2 payments and then never paid together with Return post etc.

Auto finance is one of the important retail products offered by most of the banks and more importantly by the automobile manufacturers also having a dedicated business unit for Financial Services.

Autofinance has two business segments:

1. Financing
2. Leasing

The customers can also be segmented as:

1. Private Individuals
2. Business

Other segmentations include the Old/ New vehicle.

The collections process of Auto finance portfolio is therefore not as simple compared to the other retail banking products like Personal Loans, Credit cards etc. The simple reason being that the value of the car depreciates with every passing day. This calls for higher efficiency of the collections system and processes using sophisticated models for scoring and segmentation. Furthermore, the collections process of autofinance does not end with the cancellation of the contract, but continues with further additional activities of repossession of the vehicle, selling it, adjusting the balance and litigation and recovery for the balance outstanding.

The higher volumes of cases in collections with a mix of above segments can pose extremely challenging situations for effective and optimal management of collections resources. This demands a robust scoring and segmentation model to cater to the growing needs of the collections portfolio. The risk groups for the portfolio should be categorised as High, Medium, Low and Premium.

Drivers to prioritize operational level collections.

Another very important driver in prioritizing is the segmentation on the Old / New vehicles. This variable should be used at operational level in prioritizing the actions or queue the accounts in the operational workflow system.  As we know that new cars have higher value, those accounts have to be worked before the old cars. These segmentations can be built at different levels, firstly at the scorecard level and secondly at the operational level to set up the workflows and prioritize actions.

Difference between Financing and Leasing customers

Financing and Leasing business have to be segmented differently based on the definition of the business contracts. In financing the customer will own the vehicle after the successful repayment of the loan amount. Whereas in leasing the ownership of the vehicle is retained by the leasing company and only the monthly instalment is paid for the period the vehicle is used.

The second level of segmentation is the customer type i.e. Private individuals and Business. The private individuals have largely single contracts, whereas the business customers can have multiple contracts and that makes collections on the overdue contracts even more complicated.

Therefore while designing the scorecard models; these basic segmentations have to be considered. Overlooking these can lead to lesser effective scorecards leading to ineffective risk segmentation strategies.

Stages in Collections

The various stages in Collections for Auto finance portfolio are:

Early Collections, Mid Stage Collections, Repossession, Litigation, Late stage Collections and Recoveries.

The number of scorecards in every stage can vary depending on the business requirements. The best practices recommend that the number of score cards should not be too less or too many but optimal to cover the entire portfolio for effective risk segmentation for the above mentioned phases.

Most businesses are not certain if they would go for a static scoring model or a dynamic scoring model. This largely depends on the Work flow system being used for collections treatments. The static model has a collections score for the accounts at the time when the account enters Collections i.e. 1 Dpd and follows a standard treatment paths as defined till the time it is cancelled. This model has a disadvantage that if the accounts are good and have been in collections with no payments for over 2months, the collectors are unable to take a decision on how to treat these customers. Therefore, the static scoring model has a limitation only till the initial buckets and for the midstage collections, the accounts have to be re-scored to reassess the risk on them for better segmentation and treatment. That means the accounts after getting scored in early collections will be need another scorecard for the mid stage collections to update the risk segments and then treat the accounts accordingly.

In other case, if the operational workflow system is capable of re-routing the cases based on the dynamic movements in buckets a dynamic model will be the most appropriate Best Practice

The good bad definition changes with every phase and for every scorecard in that phase. The good bad definition must be in line with the business flow e.g. the Good Bad definition for the Early collections will differ for accounts < 90 Dpd and those >= 90 Dpd for the midstage collections till cancellation. Similarly, based on the different variables for Financing and Leasing and for Private and Business customers, there will be a need of separate scorecards. This calls for altogether 4 scorecards each for Financing and Leasing as:

Financing

< 90 Dpd Scorecards:

1. Private Individuals
2. Business / Companies

>= 90 Dpd Scorecards:

1. Private Individuals
2. Business/ Companies

Similarly, the Leasing Portfolio can have 4 scorecards to covering both the business segments.

As the accounts grow from Midstage, they are ultimately cancelled and reach the Repossession stage. It is a phase, where the critical decisions to repossess the vehicle are taken. Delay in the decision can lead to further depreciation of the vehicle with every passing day. Therefore Cost Benefits analysis plays a significant role in the scorecards at this stage to assess, if the accounts need to be prioritized for an internal or external treatment. The repossession of the cars has to be prioritized based on Balance at Risk for new high value cars followed by old cars. Therefore the one segmentation level can be reduced by consolidating the portfolios to have two scorecard one each for Financing and Leasing. E.g. Financing (Private and Business) and Leasing (Private and Business) can be two scorecards covering the portfolio for repossession.

Legal collections come into play after the repossessed vehicle is priced and sold to recover the outstanding balance. The amount received after selling the vehicle is adjusted with the outstanding balance. Any positive balance left after the adjustment is due for recovery. The scorecard at this stage should be able to predict the success of a legal process. Litigation is a slow and costly process therefore it needs good analytical base especially to set up a minimum threshold for the outstanding balance to qualify for the litigation process. All other accounts with lower chances of success through litigation are written off and should be allocated to the Late stage recovery either In-house or to external DCAs (Debt Collection agencies). This stage can follow the same model as for repossession.

Recovery process is the last phase of the collections process. This can be either done In-house or externally by allocating the cases to the DCAs. The scorecards can here add value by predicting based on cost benefits analysis, if the accounts need further resources to be invested or in order to save the costs the accounts can be offered final settlements. Recovery can also have the scoring model same as legal collections as the decision is largely based on the cost benefits analysis.

The latest National retail traffic report from Experian FootFall shows a 8.3% week on week decrease in shopper numbers and a decrease of 8.6% year on year.

The daily figures reflect the fact that last year was the Queen’s Diamond Jubilee Celebrations and extended holiday. Monday and Tuesday show a year on year uplift of 6.4% and 3.2% respectively, last year both days performed poorly as most consumers focused on the celebrations and not on shopping.  The rest of the week shows a sharp decline year on year. Last year saw a huge increase as many people took the opportunity to shop during an extended break after the two day bank holiday.

Regionally only the North East regions showed an increase both week on week and year on year, all others showed a decline.   This continued positive performance for the region could be a real indicator of consumer confidence. It will be worth keeping an eye on this in future.

Unusually the Retail Park Index showed a decline year on year of 3.8% and a decline of 7.4% week on week.

Read the latest UK National Retail FootFall Weekly Report

Yesterday I was at the DMA-IDM Scotland Conference in Edinburgh. It was great to be amongst the Scottish marketing industry and have the opportunity to discuss how we can all deliver cross-channel campaigns that help us engage with today’s connected customer.

There was a great line up of speakers. We heard from Microsoft, Blippar and Random House who shared strategic and practical insights into the impact of the latest digital innovations on marketing and what this means for businesses.

Talk at the event was of how much customers are changing and why it is essential for marketers to think carefully about how they connect with customers both through online and offline channels.  What is apparent is that marketers must not focus on channels but move to personalised interactions with customers, regardless of channel.

Colin Grieves, our Head of Strategy & Propositions at Experian Marketing Services, shared his insight at the event and explained how boundaries are being broken down to truly connect with customers. At the heart of this is getting the data, insight and technology correct.

For the modern marketer this means you will need to:

• Be aware of the new data innovations that can make a difference to your business. Make sure you use the new data sources available and link your data across channels. This is essential.
• Understand all the new channels available to marketers. The new consumer is hyper-connected and the future is smart … be that from mobiles, bank cards, cars and even refrigerators.
• Effective deployment is vital. We’ve just launched our own Cross-Channel Marketing Platform, which enables brands to deliver data-driven communications across all channels, integrating real time data and customer insight to create meaningful customer conversations.
• Don’t forget the consumer! Data owners must ensure that opted-in data is at the core of their strategy and that they are data compliant.

If you want to know more, contact me, or check out how Experian can help you be a smarter marketer.

Governments are treading a fine line between adopting a programme of austerity and not knocking countries back into crippling recession. The experience of countries varies.

Within the Eurozone, Germany posted the strongest growth in 2013q1. This ‘growth’ was an unspectacular 0.1% expansion. Yet, compared to the rest of the Eurozone, it positively shines.

The UK posted slightly better growth of 0.3% in the first quarter. Mervyn King has heralded this as the beginning of a turnaround. Given recent dire labour market figures, this is a rather optimistic statement. But, at the very least, the UK does stand favourably in comparison with the larger Eurozone players. Osborne has stubbornly clung to his austerity programme despite IMF pressure to ease off on fiscal discipline and accelerate growth. Perhaps his reign of austerity has succeeded in allaying investor fears of a severe credibility downgrade. But then, for all of President Hollande’s anti-austerity rhetoric, Brussels has forced him to stick to tougher fiscal rules and raise taxes. But the only thing the French version of austerity that has achieved, apart from a rapid plummeting of Hollande’s popularity, is a triple-dip recession and unemployment that is at an all-time high.

The IMF has now softened its stance on the need for fiscal restraint and the containing of budget deficits. The priority now, they say, is stimulating growth, Keynesian-style. Indeed, this approach seems to have had an impact in the US, where after a massive and unprecedented quantitative easing programme, economic data is looking stronger and healthier than it has for a long time. But do the ailing Eurozone economies have the ability to sustain the high levels of debt that the easing off of austerity would lead to? Indeed that is a structural issue that may rear its ugly head several years down the road as it impairs economies’ long-term ability to grow. In the short term, at least, it seems governments must tread a very fine line between ensuring that does not happen and not knocking key countries back into painful and crippling recession from the brink of recovery again.

Segmentation is a powerful technique, but it can be misunderstood and misused. Therefore it’s important to have a clear idea about what segmentation should be used for.

In The 10 common traps of segmenting customers – Part 1, I talked about the following traps:

1. Segmentation is the action – not the objective
2. Too big to handle
3. The customer chimera
4. The frozen state
5. Problems with referencing

Read on to find out about more common traps that can occur when using segmentation:

6. Differentiation or just different coloured envelopes?
The best segmentation framework in the world will still not deliver a return if a business cannot conceive and execute worthwhile strategies. After all, what’s the point in having segments if the customer experience is hardly different across each one?

All too often organisations think the best use of segmentation is in creating different communications for different groups of people. Frankly, if that’s your only reason for segmentation, it’s not worth the expense. It creates minimal difference, and won’t justify the cost. At the end of the day segmentation can only pay for itself by delivering lower conversion costs, higher prices and improved margins.

True segmentation means different propositions for different customer groups, not just different coloured envelopes in their direct mail.

7. Poor resource allocation and ROI assessment
All too often organisations allocate resources by product or business function. Yet if you are serious about segmentation, you need to follow a scientific method to allocate resources and assess returns across different segments.

One challenge to this is, of course, the fact that segments are not stable. How can you allocate suitable resources if customers shift segments? The answer for many organisations is to only segment at the macro level, for example by geography, by sector, by consumer/B2B.

8. Segment bleed – this sector is not for you
Segmentation may look good on paper, but customers are forever breaking out of their segments. If someone from the ‘Young Fun’ segment takes a shine to a proposition developed for ‘Grey Professionals,’ you don’t want to turn their business down. Yet this can ultimately damage a brand, particularly in a mature market.

9. Segmentation isn’t monotheism
Some segmentation programmes take on a distinctly biblical form, with the philosophy that ‘there shall be no other segmentation, but the chosen one.’

Indeed, some segmentation initiatives make Mao’s Cultural Revolution look like liberal tree-hugging. A steering committee is set up, other approaches are outlawed and a segmentation ‘manifesto’ is created, complete with witty pen portraits of the segments. Zealous marketing staff rush from agency to agency clutching these little red books to their breasts. Any business case that supports this segmentation is signed off immediately, and a collective mania grips the whole business. Finally, someone points out that the emperor looks rather underdressed…

This approach is just plain wrong. Segmentation is not a tool, it’s a tool-set. Yet if all you’ve got in your hand is a hammer, everything looks like a nail.

Segmentation is most powerful when it addresses a specific problem. And as most businesses face many problems, segmentation must be multi-dimensional. Value, needs, behaviour, product, demographics, customer state, preference, credit – segmentation can take any number of approaches, making your organisation as flexible as possible to meet business challenges.

One hurdle to overcome is the senior executive’s preference for simpler, easy to understand concepts. Today’s marketer has to be able to explain and demonstrate the benefits of multi-dimensionality against seductively simpler segmentation.

10. Organising based on customer segmentation
To become truly customer focused, many businesses flirt with the notion of using segmentation to create an organisational model.

This may be a laudable idea, but it creates serious problems:

• Segments have to be referenced and stable to achieve it, and costs begin to proliferate the more segments you have.
• As revenue and cost-reporting are normally product-based, this then has to be engineered into a segment-based view.
• Allocating resources and customers to segments means setting up complex business rules.

Very few large organisations have introduced truly customer segment-based organisational models. Yet there are many businesses with excellent track records in value management and customer commitment. The truth is that customer focus delivers success but you don’t have to organise around customer segments to be customer focused.

The latest National retail traffic report from Experian FootFall shows a 9.2% week on week uplift in shopper traffic and a decline of 3.1% year on year. Bank Holiday Monday delivered a drop of 16.9% year on year with many potential consumers choosing to stay home and make the most of a long awaited injection of hot and sunny weather across the long weekend.

This time last year saw the start of the Jubilee celebrations, which pushed the Bank Holiday to the following week, although festivities began during the weekend of 2^nd and 3^rd June. This is evidenced in the year on year decline of this weekend’s figures which were down by 9.9% and 7.6% for Saturday and Sunday respectively.

Perhaps the most notable regional decline took place in London, which would have been the hub of jubilee activity last year. Here footfall was down by 5.5% against last year. Across the regions however, we see mixed results with store visitor numbers up in four out of 11 regions overall.

Whilst there have been reports of some uplift in consumer confidence this week, Kingfisher posting a first quarter profit fall, blaming poor weather on a reduction in footfall. Meanwhile, the Retail Park sector provided another clear demonstration of resilience and growth on last year’s traffic levels with a respectable 0.6% year on year rise. The week on week increase however was some way behind the National result at 3.8%.

Read the full index report